Podcast and Media Appearances

Is the world ready to learn from its AI twin?

From AI teachers and mentors to AI coaches, therapists, and even personal trainers — intelligent digital companions are entering every corner of our lives. But how far can (or should) they go?

Join Anna Gandrabura and Galina Fendikevich for the second session in our November AI Workshop Series — a live discussion exploring the rise of AI-powered “human twins” and what this means for education, personal growth, and the workplace.

We’ll explore questions like:

• Can an AI teacher replace human empathy?

• Would you open up to an AI therapist?

• How can an AI coach or mentor scale access to guidance — without losing authenticity?

In this interactive session, we’ll unpack:

⚖️ The pros and cons of AI teachers, mentors, and personal assistants

🧩 The psychology of trust and connection with digital humans

🚀 How “AI twins” could redefine learning, leadership, and creativity

💬 Real examples of AI tutors and companions already shaping the future

Expect an honest, balanced conversation — equal parts curiosity and critical thinking.

Curious about how AI can actually make your workday easier—not just more complicated?

Join Anna Gandrabura and Galina Fendikevich for a live, hands-on workshop where we’ll show you the Top 3 AI Tools you’ll wish you’d discovered sooner—tools that save hours every single week. We’ll walk you through a real-world workflow—from meetings and brainstorming to writing and delivery—and show you how:

⚡ Fireflies captures, organizes, and summarizes your meetings effortlessly

🎙️ Castmagic turns audio and video into polished content in minutes

💬 ChatGPT / Copilot helps you plan, write, and create smarter, faster, and better

By the end of this session, you’ll have:

✅ A clear understanding of when and how to use each tool

✅ Practical prompts and workflows to apply right away

✅ A mini AI productivity system you can build on

This session is perfect for educators, professionals, founders, and creators who want to work smarter, not harder.

No tech background required, just curiosity.

Everyone is talking about AI adoption, but few people are showing what it actually looks like on the inside.

In this third session of our AI Workshop Series, join Anna Gandrabura and Galina Fendikevich for a hands-on conversation about practical AI integration — from small team automations to full-scale workflows that transform how a company operates.

We’ll share real examples from Anna’s company English For IT™, where AI isn’t just a buzzword — it’s part of the daily routine: Teaching & Learning: using ChatGPT and custom AI tools to enhance lessons, run internal training, and upskill instructors. Operations: automating communication, data entry, and client follow-ups. Innovation: developing and “vibe-coding” new edu-tech products powered by AI.

Together, we’ll explore how any business — from education to marketing to tech to manufacturing — can start integrating AI today, sustainably and creatively.

What You’ll Learn

✔️How to identify high-impact areas for AI automation in your workflow

✔️Tools and prompts to bridge creativity and productivity

✔️Real lessons learned from implementing AI in a global businesses

✔️What works, what doesn’t, and what to avoid when bringing AI to your team

Expect tons of AI insights and practical tips.

Guest: Galina Fendikevich

Episode Theme: Using AI tools in Product Marketing

Key Discussion Points:

Tool Selection and Integration, Enhancing Customer Insights, Optimizing Campaign Performance, Overcoming Adoption Barriers.

•Tool Selection and Integration: Learn how Galina decides which AI tools are best suited for enhancing product marketing strategies and the crucial criteria for selection and integration.

•Enhancing Customer Insights: Discover the powerful ways AI can uncover deeper customer insights and how to translate these findings into more effective messaging and product positioning.

•Optimizing Campaign Performance: Gain valuable insights from real-world examples of how AI-driven automation and analytics have improved the efficiency and impact of product marketing campaigns.

•Overcoming Adoption Barriers: Understand the common challenges encountered when adopting AI tools in marketing workflows and practical strategies to overcome these hurdles and achieve measurable results.

Plus, don't miss the rapid-fire round where we get to know Galina's favorite marketing tools, underrated skills in product marketing, biggest mistakes to avoid, and much more!

Tune in to gain actionable insights on leveraging AI to power your product marketing efforts and stay ahead in this rapidly evolving landscape. Subscribe for more expert discussions and strategies to excel in your product marketing journey!

Anthropic just raised $1.5B from Blackstone and Goldman Sachs. OpenAI raised $4B from TPG, Bain, and Advent the same day.

Both labs are building delivery organizations, not just sales channels, explicitly modeled on Palantir’s forward-deployed engineer (FDE) playbook.

Here’s the problem: What you’re really buying is a custom AI build that needs ongoing FDE support to keep working. That's lock-in dressed as transformation.

Nvidia's Jensen Huang says a $500K engineer who doesn't burn $250K in AI tokens is underperforming. Morgan Stanley and Goldman are backing the framing. "Tokens consumed" is becoming the new AI productivity KPI.

But in cost sensitive regulated industries, burning tokens doesn't mean generating more value. In fact, if the cost of the employee plus tokens used doesn't lead to direct revenue growth to cover the cost of this "AI enabled" employee, then you're upside down on your ROI and your CFO is going to shut it down fast.

Next week on Humans in the Loop, we're asking: is the token economy real, and how should boards measure it?

130 questions on your vendor security review. You checked SOC 2, data residency, encryption, incident response.

Zero about which foundation model is making the decisions. Why does it matter?

That's the question and the gap we're opening up in HITL Episode 5.

When Elon's $1B automation mistake turned into a tent full of humans In 2018, Tesla was trying to scale Model 3 production. We were just getting started, but the targets were impossibly aggressive.

We had to hyper-scale before we even figured out how to put a battery pack together. Elon's answer was to fully automate the Fremont factory, and it was a complete disaster that almost burned the company down.

As a solution, we ended up setting up a giant tent outside in the parking lot, throwing humans at the problem to keep cars moving off the line.

But the BEST part is how we fixed it: Elon lived on the factory floor, embedding himself into every single bottleneck on the assembly line until he understood what was actually breaking and why.

That experience taught me two things about how we should approach AI today: the cautionary tale of what goes wrong when you try to scale before you understand the work, and the best practice of what it looks like when you actually do it right.

One company invested $1 billion training its people to use AI responsibly. Then laid them off.

The other mandated AI adoption across its engineering org, watched it trigger a 13-hour outage, and quietly edited the internal memo before the all-hands meeting.

In Episode 3 of Human in the Loop, we break down what PwC and Amazon actually did, what it cost them, and what it means for the humans still in the loop: the ones being told they're responsible for code they didn't write.

This week, we are looking at the companies sitting on the most to lose, and what happens when AI forces them to confront that.

👉 Marriott told Wall Street AI was no big deal. Then quietly rewired the entire company.

👉 Hallmark spent 115 years selling the idea that effort is the point. Then AI showed up and complicated that story in ways they couldn't ignore.

👉 IBM's mainframe code is being refactored as we speak, and what that means for enterprise modernization is more interesting than the headlines.

Trivia, real data, and case studies that are harder to dismiss than the usual AI success theater.

Most companies are investing in AI. Very few can show you what it's actually doing for their business. We went through Q4 earnings calls across Fortune 500 insurance, financial services, healthcare, and logistics to find out who's really moving the needle, and more importantly, why.